Comparing Dataset A and Dataset B

Dataset statistics

	Dataset A	Dataset B
Number of variables	12	12
Number of observations	446	446
Missing cells	435	432
Missing cells (%)	8.1%	8.1%
Duplicate rows	0	0
Duplicate rows (%)	0.0%	0.0%
Total size in memory	45.3 KiB	45.3 KiB
Average record size in memory	104.0 B	104.0 B

Variable types

	Dataset A	Dataset B
Numeric	5	5
Categorical	4	4
Text	3	3

Alerts

Dataset A	Dataset B
`Age` has 88 (19.7%) missing values	`Age` has 88 (19.7%) missing values	Missing
`Cabin` has 346 (77.6%) missing values	`Cabin` has 344 (77.1%) missing values	Missing
`PassengerId` has unique values	`PassengerId` has unique values	Unique
`Name` has unique values	`Name` has unique values	Unique
`SibSp` has 309 (69.3%) zeros	`SibSp` has 298 (66.8%) zeros	Zeros
`Parch` has 336 (75.3%) zeros	`Parch` has 332 (74.4%) zeros	Zeros
`Fare` has 6 (1.3%) zeros	`Fare` has 5 (1.1%) zeros	Zeros

Reproduction

	Dataset A	Dataset B
Analysis started	2024-01-08 15:05:20.561676	2024-01-08 15:05:24.828375
Analysis finished	2024-01-08 15:05:24.827309	2024-01-08 15:05:28.722837
Duration	4.27 seconds	3.89 seconds
Software version	ydata-profiling v0.0.dev0	ydata-profiling v0.0.dev0
Download configuration	config.json	config.json

PassengerId
Real number (ℝ)

	Dataset A	Dataset B
Distinct	446	446
Distinct (%)	100.0%	100.0%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	447.11659	447.1704

	Dataset A	Dataset B
Minimum	1	1
Maximum	890	891
Zeros	0	0
Zeros (%)	0.0%	0.0%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	1	1
5-th percentile	37.25	40.5
Q1	239.25	217.25
median	446.5	433
Q3	666.75	689.5
95-th percentile	841.75	856.75
Maximum	890	891
Range	889	890
Interquartile range (IQR)	427.5	472.25

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	254.98656	267.31341
Coefficient of variation (CV)	0.57029099	0.5977887
Kurtosis	-1.1483252	-1.2855882
Mean	447.11659	447.1704
Median Absolute Deviation (MAD)	212.5	233.5
Skewness	-0.02202314	0.031052275
Sum	199414	199438
Variance	65018.148	71456.461
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
546	1	0.2%
849	1	0.2%
868	1	0.2%
516	1	0.2%
888	1	0.2%
232	1	0.2%
533	1	0.2%
421	1	0.2%
334	1	0.2%
543	1	0.2%
Other values (436)	436	97.8%

Value	Count	Frequency (%)
746	1	0.2%
376	1	0.2%
204	1	0.2%
535	1	0.2%
243	1	0.2%
57	1	0.2%
819	1	0.2%
737	1	0.2%
195	1	0.2%
555	1	0.2%
Other values (436)	436	97.8%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	1	0.2%
2	1	0.2%
5	1	0.2%
6	1	0.2%
7	1	0.2%
9	1	0.2%
10	1	0.2%
12	1	0.2%
13	1	0.2%
14	1	0.2%

Value	Count	Frequency (%)
1	1	0.2%
5	1	0.2%
6	1	0.2%
10	1	0.2%
11	1	0.2%
13	1	0.2%
14	1	0.2%
15	1	0.2%
18	1	0.2%
19	1	0.2%

Value	Count	Frequency (%)
1	1	0.2%
5	1	0.2%
6	1	0.2%
10	1	0.2%
11	1	0.2%
13	1	0.2%
14	1	0.2%
15	1	0.2%
18	1	0.2%
19	1	0.2%

Value	Count	Frequency (%)
1	1	0.2%
2	1	0.2%
5	1	0.2%
6	1	0.2%
7	1	0.2%
9	1	0.2%
10	1	0.2%
12	1	0.2%
13	1	0.2%
14	1	0.2%

Survived
Categorical

	Dataset A	Dataset B
Distinct	2	2
Distinct (%)	0.4%	0.4%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

0	265
1	181

0	296
1	150

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	446	446
Distinct characters	2	2
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	0	0
2nd row	0	0
3rd row	1	0
4th row	0	0
5th row	1	1

Common Values

Value	Count	Frequency (%)
0	265	59.4%
1	181	40.6%

Value	Count	Frequency (%)
0	296	66.4%
1	150	33.6%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
0	265	59.4%
1	181	40.6%

Value	Count	Frequency (%)
0	296	66.4%
1	150	33.6%

Most occurring characters

Value	Count	Frequency (%)
0	265	59.4%
1	181	40.6%

Value	Count	Frequency (%)
0	296	66.4%
1	150	33.6%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	446	100.0%

Value	Count	Frequency (%)
Decimal Number	446	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	265	59.4%
1	181	40.6%

Value	Count	Frequency (%)
0	296	66.4%
1	150	33.6%

Most occurring scripts

Value	Count	Frequency (%)
Common	446	100.0%

Value	Count	Frequency (%)
Common	446	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	265	59.4%
1	181	40.6%

Value	Count	Frequency (%)
0	296	66.4%
1	150	33.6%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	446	100.0%

Value	Count	Frequency (%)
ASCII	446	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	265	59.4%
1	181	40.6%

Value	Count	Frequency (%)
0	296	66.4%
1	150	33.6%

Pclass
Categorical

	Dataset A	Dataset B
Distinct	3	3
Distinct (%)	0.7%	0.7%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

3	243
1	104
2	99

3	252
1	108
2	86

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	446	446
Distinct characters	3	3
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	1	1
2nd row	3	2
3rd row	3	1
4th row	3	3
5th row	2	3

Common Values

Value	Count	Frequency (%)
3	243	54.5%
1	104	23.3%
2	99	22.2%

Value	Count	Frequency (%)
3	252	56.5%
1	108	24.2%
2	86	19.3%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
3	243	54.5%
1	104	23.3%
2	99	22.2%

Value	Count	Frequency (%)
3	252	56.5%
1	108	24.2%
2	86	19.3%

Most occurring characters

Value	Count	Frequency (%)
3	243	54.5%
1	104	23.3%
2	99	22.2%

Value	Count	Frequency (%)
3	252	56.5%
1	108	24.2%
2	86	19.3%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	446	100.0%

Value	Count	Frequency (%)
Decimal Number	446	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
3	243	54.5%
1	104	23.3%
2	99	22.2%

Value	Count	Frequency (%)
3	252	56.5%
1	108	24.2%
2	86	19.3%

Most occurring scripts

Value	Count	Frequency (%)
Common	446	100.0%

Value	Count	Frequency (%)
Common	446	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
3	243	54.5%
1	104	23.3%
2	99	22.2%

Value	Count	Frequency (%)
3	252	56.5%
1	108	24.2%
2	86	19.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	446	100.0%

Value	Count	Frequency (%)
ASCII	446	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
3	243	54.5%
1	104	23.3%
2	99	22.2%

Value	Count	Frequency (%)
3	252	56.5%
1	108	24.2%
2	86	19.3%

Name
['Text', 'Text']

	Dataset A	Dataset B
Distinct	446	446
Distinct (%)	100.0%	100.0%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	67	57
Median length	50	46
Mean length	26.526906	26.737668
Min length	12	13

Characters and Unicode

	Dataset A	Dataset B
Total characters	11831	11925
Distinct characters	60	59
Distinct categories	7	7 ?
Distinct scripts	2	2 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	446	446 ?
Unique (%)	100.0%	100.0%

Sample

	Dataset A	Dataset B
1st row	Nicholson, Mr. Arthur Ernest	Crosby, Capt. Edward Gifford
2nd row	Nosworthy, Mr. Richard Cater	Norman, Mr. Robert Douglas
3rd row	de Mulder, Mr. Theodore	Robbins, Mr. Victor
4th row	Asplund, Master. Clarence Gustaf Hugo	Petterson, Mr. Johan Emil
5th row	Richards, Master. George Sibley	Andersen-Jensen, Miss. Carla Christine Nielsine

Value	Count	Frequency (%)
mr	264	14.7%
miss	98	5.5%
mrs	60	3.3%
william	30	1.7%
henry	21	1.2%
master	20	1.1%
john	20	1.1%
thomas	15	0.8%
james	14	0.8%
mary	12	0.7%
Other values (876)	1243	69.2%

Value	Count	Frequency (%)
mr	271	15.1%
miss	97	5.4%
mrs	53	2.9%
william	31	1.7%
john	22	1.2%
henry	21	1.2%
master	17	0.9%
james	13	0.7%
george	12	0.7%
edward	10	0.6%
Other values (873)	1252	69.6%

Most occurring characters

Value	Count	Frequency (%)
	1351	11.4%
r	974	8.2%
e	872	7.4%
a	814	6.9%
s	655	5.5%
i	653	5.5%
n	645	5.5%
M	572	4.8%
l	514	4.3%
o	459	3.9%
Other values (50)	4322	36.5%

Value	Count	Frequency (%)
	1354	11.4%
r	972	8.2%
e	856	7.2%
a	813	6.8%
i	672	5.6%
n	661	5.5%
s	643	5.4%
M	558	4.7%
l	533	4.5%
o	495	4.2%
Other values (49)	4368	36.6%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7585	64.1%
Uppercase Letter	1811	15.3%
Space Separator	1351	11.4%
Other Punctuation	949	8.0%
Open Punctuation	66	0.6%
Close Punctuation	66	0.6%
Dash Punctuation	3	< 0.1%

Value	Count	Frequency (%)
Lowercase Letter	7695	64.5%
Uppercase Letter	1806	15.1%
Space Separator	1354	11.4%
Other Punctuation	945	7.9%
Close Punctuation	60	0.5%
Open Punctuation	60	0.5%
Dash Punctuation	5	< 0.1%

Most frequent character per category

Space Separator

Value	Count	Frequency (%)
	1351	100.0%

Value	Count	Frequency (%)
	1354	100.0%

Lowercase Letter

Value	Count	Frequency (%)
r	974	12.8%
e	872	11.5%
a	814	10.7%
s	655	8.6%
i	653	8.6%
n	645	8.5%
l	514	6.8%
o	459	6.1%
t	321	4.2%
h	261	3.4%
Other values (16)	1417	18.7%

Value	Count	Frequency (%)
r	972	12.6%
e	856	11.1%
a	813	10.6%
i	672	8.7%
n	661	8.6%
s	643	8.4%
l	533	6.9%
o	495	6.4%
t	322	4.2%
d	252	3.3%
Other values (16)	1476	19.2%

Uppercase Letter

Value	Count	Frequency (%)
M	572	31.6%
A	119	6.6%
H	106	5.9%
J	103	5.7%
E	86	4.7%
S	85	4.7%
B	82	4.5%
C	72	4.0%
W	71	3.9%
R	62	3.4%
Other values (15)	453	25.0%

Value	Count	Frequency (%)
M	558	30.9%
A	125	6.9%
H	113	6.3%
J	98	5.4%
S	95	5.3%
E	84	4.7%
C	83	4.6%
W	69	3.8%
D	62	3.4%
B	60	3.3%
Other values (15)	459	25.4%

Other Punctuation

Value	Count	Frequency (%)
.	446	47.0%
,	446	47.0%
"	50	5.3%
'	6	0.6%
/	1	0.1%

Value	Count	Frequency (%)
,	446	47.2%
.	446	47.2%
"	50	5.3%
'	3	0.3%

Open Punctuation

Value	Count	Frequency (%)
(	66	100.0%

Value	Count	Frequency (%)
(	60	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	66	100.0%

Value	Count	Frequency (%)
)	60	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	3	100.0%

Value	Count	Frequency (%)
-	5	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	9396	79.4%
Common	2435	20.6%

Value	Count	Frequency (%)
Latin	9501	79.7%
Common	2424	20.3%

Most frequent character per script

Common

Value	Count	Frequency (%)
	1351	55.5%
.	446	18.3%
,	446	18.3%
(	66	2.7%
)	66	2.7%
"	50	2.1%
'	6	0.2%
-	3	0.1%
/	1	< 0.1%

Value	Count	Frequency (%)
	1354	55.9%
,	446	18.4%
.	446	18.4%
)	60	2.5%
(	60	2.5%
"	50	2.1%
-	5	0.2%
'	3	0.1%

Latin

Value	Count	Frequency (%)
r	974	10.4%
e	872	9.3%
a	814	8.7%
s	655	7.0%
i	653	6.9%
n	645	6.9%
M	572	6.1%
l	514	5.5%
o	459	4.9%
t	321	3.4%
Other values (41)	2917	31.0%

Value	Count	Frequency (%)
r	972	10.2%
e	856	9.0%
a	813	8.6%
i	672	7.1%
n	661	7.0%
s	643	6.8%
M	558	5.9%
l	533	5.6%
o	495	5.2%
t	322	3.4%
Other values (41)	2976	31.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	11831	100.0%

Value	Count	Frequency (%)
ASCII	11925	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	1351	11.4%
r	974	8.2%
e	872	7.4%
a	814	6.9%
s	655	5.5%
i	653	5.5%
n	645	5.5%
M	572	4.8%
l	514	4.3%
o	459	3.9%
Other values (50)	4322	36.5%

Value	Count	Frequency (%)
	1354	11.4%
r	972	8.2%
e	856	7.2%
a	813	6.8%
i	672	5.6%
n	661	5.5%
s	643	5.4%
M	558	4.7%
l	533	4.5%
o	495	4.2%
Other values (49)	4368	36.6%

Sex
Categorical

	Dataset A	Dataset B
Distinct	2	2
Distinct (%)	0.4%	0.4%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

male	289
female	157

male	296
female	150

Length

	Dataset A	Dataset B
Max length	6	6
Median length	4	4
Mean length	4.7040359	4.6726457
Min length	4	4

Characters and Unicode

	Dataset A	Dataset B
Total characters	2098	2084
Distinct characters	5	5
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	male	male
2nd row	male	male
3rd row	male	male
4th row	male	male
5th row	male	female

Common Values

Value	Count	Frequency (%)
male	289	64.8%
female	157	35.2%

Value	Count	Frequency (%)
male	296	66.4%
female	150	33.6%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
male	289	64.8%
female	157	35.2%

Value	Count	Frequency (%)
male	296	66.4%
female	150	33.6%

Most occurring characters

Value	Count	Frequency (%)
e	603	28.7%
m	446	21.3%
a	446	21.3%
l	446	21.3%
f	157	7.5%

Value	Count	Frequency (%)
e	596	28.6%
m	446	21.4%
a	446	21.4%
l	446	21.4%
f	150	7.2%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	2098	100.0%

Value	Count	Frequency (%)
Lowercase Letter	2084	100.0%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	603	28.7%
m	446	21.3%
a	446	21.3%
l	446	21.3%
f	157	7.5%

Value	Count	Frequency (%)
e	596	28.6%
m	446	21.4%
a	446	21.4%
l	446	21.4%
f	150	7.2%

Most occurring scripts

Value	Count	Frequency (%)
Latin	2098	100.0%

Value	Count	Frequency (%)
Latin	2084	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	603	28.7%
m	446	21.3%
a	446	21.3%
l	446	21.3%
f	157	7.5%

Value	Count	Frequency (%)
e	596	28.6%
m	446	21.4%
a	446	21.4%
l	446	21.4%
f	150	7.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2098	100.0%

Value	Count	Frequency (%)
ASCII	2084	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	603	28.7%
m	446	21.3%
a	446	21.3%
l	446	21.3%
f	157	7.5%

Value	Count	Frequency (%)
e	596	28.6%
m	446	21.4%
a	446	21.4%
l	446	21.4%
f	150	7.2%

Age
Real number (ℝ)

	Dataset A	Dataset B
Distinct	73	74
Distinct (%)	20.4%	20.7%
Missing	88	88
Missing (%)	19.7%	19.7%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	29.365922	29.526536

	Dataset A	Dataset B
Minimum	0.67	0.42
Maximum	74	80
Zeros	0	0
Zeros (%)	0.0%	0.0%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0.67	0.42
5-th percentile	4	4
Q1	20	19.25
median	28	28
Q3	37.75	38
95-th percentile	58	57.15
Maximum	74	80
Range	73.33	79.58
Interquartile range (IQR)	17.75	18.75

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	14.59244	15.003293
Coefficient of variation (CV)	0.49691747	0.50812913
Kurtosis	0.17882939	0.3822428
Mean	29.365922	29.526536
Median Absolute Deviation (MAD)	8	9
Skewness	0.429838	0.50290276
Sum	10513	10570.5
Variance	212.93929	225.09881
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
30	15	3.4%
22	14	3.1%
21	14	3.1%
19	13	2.9%
24	13	2.9%
18	13	2.9%
25	12	2.7%
29	11	2.5%
28	11	2.5%
27	11	2.5%
Other values (63)	231	51.8%
(Missing)	88	19.7%

Value	Count	Frequency (%)
25	16	3.6%
19	15	3.4%
18	13	2.9%
28	13	2.9%
16	13	2.9%
24	12	2.7%
27	12	2.7%
22	12	2.7%
26	11	2.5%
32	11	2.5%
Other values (64)	230	51.6%
(Missing)	88	19.7%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0.67	1	0.2%
0.75	2	0.4%
0.83	1	0.2%
1	3	0.7%
2	4	0.9%
3	4	0.9%
4	6	1.3%
5	3	0.7%
7	2	0.4%
8	3	0.7%

Value	Count	Frequency (%)
0.42	1	0.2%
0.75	2	0.4%
0.83	2	0.4%
0.92	1	0.2%
1	3	0.7%
2	6	1.3%
3	1	0.2%
4	5	1.1%
5	4	0.9%
6	1	0.2%

Value	Count	Frequency (%)
0.42	1	0.2%
0.75	2	0.4%
0.83	2	0.4%
0.92	1	0.2%
1	3	0.7%
2	6	1.3%
3	1	0.2%
4	5	1.1%
5	4	0.9%
6	1	0.2%

Value	Count	Frequency (%)
0.67	1	0.2%
0.75	2	0.4%
0.83	1	0.2%
1	3	0.7%
2	4	0.9%
3	4	0.9%
4	6	1.3%
5	3	0.7%
7	2	0.4%
8	3	0.7%

SibSp
Real number (ℝ)

	Dataset A	Dataset B
Distinct	7	7
Distinct (%)	1.6%	1.6%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	0.52017937	0.56502242

	Dataset A	Dataset B
Minimum	0	0
Maximum	8	8
Zeros	309	298
Zeros (%)	69.3%	66.8%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	0	0
Q1	0	0
median	0	0
Q3	1	1
95-th percentile	2	3
Maximum	8	8
Range	8	8
Interquartile range (IQR)	1	1

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	1.1310911	1.156918
Coefficient of variation (CV)	2.1744252	2.0475612
Kurtosis	18.17709	16.229817
Mean	0.52017937	0.56502242
Median Absolute Deviation (MAD)	0	0
Skewness	3.763717	3.5364268
Sum	232	252
Variance	1.2793672	1.3384592
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
0	309	69.3%
1	99	22.2%
2	16	3.6%
4	11	2.5%
3	5	1.1%
8	4	0.9%
5	2	0.4%

Value	Count	Frequency (%)
0	298	66.8%
1	104	23.3%
2	20	4.5%
4	10	2.2%
3	7	1.6%
8	4	0.9%
5	3	0.7%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	309	69.3%
1	99	22.2%
2	16	3.6%
3	5	1.1%
4	11	2.5%
5	2	0.4%
8	4	0.9%

Value	Count	Frequency (%)
0	298	66.8%
1	104	23.3%
2	20	4.5%
3	7	1.6%
4	10	2.2%
5	3	0.7%
8	4	0.9%

Value	Count	Frequency (%)
0	298	66.8%
1	104	23.3%
2	20	4.5%
3	7	1.6%
4	10	2.2%
5	3	0.7%
8	4	0.9%

Value	Count	Frequency (%)
0	309	69.3%
1	99	22.2%
2	16	3.6%
3	5	1.1%
4	11	2.5%
5	2	0.4%
8	4	0.9%

Parch
Real number (ℝ)

	Dataset A	Dataset B
Distinct	6	6
Distinct (%)	1.3%	1.3%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	0.38340807	0.43049327

	Dataset A	Dataset B
Minimum	0	0
Maximum	5	5
Zeros	336	332
Zeros (%)	75.3%	74.4%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	0	0
Q1	0	0
median	0	0
Q3	0	1
95-th percentile	2	2
Maximum	5	5
Range	5	5
Interquartile range (IQR)	0	1

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	0.77232644	0.88094088
Coefficient of variation (CV)	2.0143719	2.0463523
Kurtosis	7.3553721	8.1877072
Mean	0.38340807	0.43049327
Median Absolute Deviation (MAD)	0	0
Skewness	2.4232583	2.6097847
Sum	171	192
Variance	0.59648813	0.77605683
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=6)

Value	Count	Frequency (%)
0	336	75.3%
1	60	13.5%
2	45	10.1%
5	2	0.4%
4	2	0.4%
3	1	0.2%

Value	Count	Frequency (%)
0	332	74.4%
1	59	13.2%
2	44	9.9%
5	5	1.1%
3	4	0.9%
4	2	0.4%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	336	75.3%
1	60	13.5%
2	45	10.1%
3	1	0.2%
4	2	0.4%
5	2	0.4%

Value	Count	Frequency (%)
0	332	74.4%
1	59	13.2%
2	44	9.9%
3	4	0.9%
4	2	0.4%
5	5	1.1%

Value	Count	Frequency (%)
0	332	74.4%
1	59	13.2%
2	44	9.9%
3	4	0.9%
4	2	0.4%
5	5	1.1%

Value	Count	Frequency (%)
0	336	75.3%
1	60	13.5%
2	45	10.1%
3	1	0.2%
4	2	0.4%
5	2	0.4%

Ticket
['Text', 'Text']

	Dataset A	Dataset B
Distinct	382	382
Distinct (%)	85.7%	85.7%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	18	18
Median length	17	17
Mean length	6.7802691	6.7242152
Min length	3	4

Characters and Unicode

	Dataset A	Dataset B
Total characters	3024	2999
Distinct characters	35	31
Distinct categories	5	5 ?
Distinct scripts	2	2 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	333	332 ?
Unique (%)	74.7%	74.4%

Sample

	Dataset A	Dataset B
1st row	693	WE/P 5735
2nd row	A/4. 39886	218629
3rd row	345774	PC 17757
4th row	347077	347076
5th row	29106	350046

Value	Count	Frequency (%)
pc	33	5.7%
c.a	17	3.0%
a/5	8	1.4%
ca	7	1.2%
1601	5	0.9%
ston/o	5	0.9%
2	5	0.9%
sc/paris	5	0.9%
ston/o2	5	0.9%
a/4	5	0.9%
Other values (407)	479	83.4%

Value	Count	Frequency (%)
pc	28	5.0%
c.a	14	2.5%
a/5	8	1.4%
ca	7	1.3%
347082	6	1.1%
w./c	6	1.1%
sc/paris	5	0.9%
3101295	4	0.7%
2	4	0.7%
ston/o	4	0.7%
Other values (400)	473	84.6%

Most occurring characters

Value	Count	Frequency (%)
3	361	11.9%
1	343	11.3%
2	296	9.8%
7	249	8.2%
4	228	7.5%
6	214	7.1%
0	205	6.8%
5	198	6.5%
8	153	5.1%
9	153	5.1%
Other values (25)	624	20.6%

Value	Count	Frequency (%)
3	367	12.2%
1	353	11.8%
2	296	9.9%
7	250	8.3%
4	236	7.9%
6	213	7.1%
0	204	6.8%
5	183	6.1%
9	164	5.5%
8	154	5.1%
Other values (21)	579	19.3%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	2400	79.4%
Uppercase Letter	323	10.7%
Other Punctuation	156	5.2%
Space Separator	128	4.2%
Lowercase Letter	17	0.6%

Value	Count	Frequency (%)
Decimal Number	2420	80.7%
Uppercase Letter	312	10.4%
Other Punctuation	146	4.9%
Space Separator	113	3.8%
Lowercase Letter	8	0.3%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
3	361	15.0%
1	343	14.3%
2	296	12.3%
7	249	10.4%
4	228	9.5%
6	214	8.9%
0	205	8.5%
5	198	8.2%
8	153	6.4%
9	153	6.4%

Value	Count	Frequency (%)
3	367	15.2%
1	353	14.6%
2	296	12.2%
7	250	10.3%
4	236	9.8%
6	213	8.8%
0	204	8.4%
5	183	7.6%
9	164	6.8%
8	154	6.4%

Space Separator

Value	Count	Frequency (%)
	128	100.0%

Value	Count	Frequency (%)
	113	100.0%

Other Punctuation

Value	Count	Frequency (%)
.	108	69.2%
/	48	30.8%

Value	Count	Frequency (%)
.	100	68.5%
/	46	31.5%

Uppercase Letter

Value	Count	Frequency (%)
C	86	26.6%
P	54	16.7%
A	46	14.2%
O	43	13.3%
S	37	11.5%
N	16	5.0%
T	15	4.6%
W	6	1.9%
F	5	1.5%
I	4	1.2%
Other values (6)	11	3.4%

Value	Count	Frequency (%)
C	75	24.0%
P	49	15.7%
O	47	15.1%
A	39	12.5%
S	34	10.9%
N	18	5.8%
T	17	5.4%
W	10	3.2%
Q	7	2.2%
I	5	1.6%
Other values (4)	11	3.5%

Lowercase Letter

Value	Count	Frequency (%)
a	5	29.4%
s	4	23.5%
r	3	17.6%
i	3	17.6%
l	1	5.9%
e	1	5.9%

Value	Count	Frequency (%)
a	2	25.0%
r	2	25.0%
i	2	25.0%
s	2	25.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	2684	88.8%
Latin	340	11.2%

Value	Count	Frequency (%)
Common	2679	89.3%
Latin	320	10.7%

Most frequent character per script

Common

Value	Count	Frequency (%)
3	361	13.5%
1	343	12.8%
2	296	11.0%
7	249	9.3%
4	228	8.5%
6	214	8.0%
0	205	7.6%
5	198	7.4%
8	153	5.7%
9	153	5.7%
Other values (3)	284	10.6%

Value	Count	Frequency (%)
3	367	13.7%
1	353	13.2%
2	296	11.0%
7	250	9.3%
4	236	8.8%
6	213	8.0%
0	204	7.6%
5	183	6.8%
9	164	6.1%
8	154	5.7%
Other values (3)	259	9.7%

Latin

Value	Count	Frequency (%)
C	86	25.3%
P	54	15.9%
A	46	13.5%
O	43	12.6%
S	37	10.9%
N	16	4.7%
T	15	4.4%
W	6	1.8%
F	5	1.5%
a	5	1.5%
Other values (12)	27	7.9%

Value	Count	Frequency (%)
C	75	23.4%
P	49	15.3%
O	47	14.7%
A	39	12.2%
S	34	10.6%
N	18	5.6%
T	17	5.3%
W	10	3.1%
Q	7	2.2%
I	5	1.6%
Other values (8)	19	5.9%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	3024	100.0%

Value	Count	Frequency (%)
ASCII	2999	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
3	361	11.9%
1	343	11.3%
2	296	9.8%
7	249	8.2%
4	228	7.5%
6	214	7.1%
0	205	6.8%
5	198	6.5%
8	153	5.1%
9	153	5.1%
Other values (25)	624	20.6%

Value	Count	Frequency (%)
3	367	12.2%
1	353	11.8%
2	296	9.9%
7	250	8.3%
4	236	7.9%
6	213	7.1%
0	204	6.8%
5	183	6.1%
9	164	5.5%
8	154	5.1%
Other values (21)	579	19.3%

Fare
Real number (ℝ)

	Dataset A	Dataset B
Distinct	181	183
Distinct (%)	40.6%	41.0%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	33.656978	32.819955

	Dataset A	Dataset B
Minimum	0	0
Maximum	512.3292	512.3292
Zeros	6	5
Zeros (%)	1.3%	1.1%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	7.2292	7.2292
Q1	7.8958	7.8958
median	14.25415	14.4542
Q3	31	31.275
95-th percentile	118.31875	118.31875
Maximum	512.3292	512.3292
Range	512.3292	512.3292
Interquartile range (IQR)	23.1042	23.3792

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	54.525694	52.5157
Coefficient of variation (CV)	1.6200413	1.600115
Kurtosis	30.009989	34.233795
Mean	33.656978	32.819955
Median Absolute Deviation (MAD)	6.71875	6.7667
Skewness	4.6670045	4.9406518
Sum	15011.012	14637.7
Variance	2973.0513	2757.8988
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
13	20	4.5%
7.8958	19	4.3%
10.5	18	4.0%
7.75	17	3.8%
8.05	17	3.8%
26	13	2.9%
7.775	11	2.5%
7.25	10	2.2%
7.925	10	2.2%
7.2292	9	2.0%
Other values (171)	302	67.7%

Value	Count	Frequency (%)
7.8958	21	4.7%
13	20	4.5%
7.75	16	3.6%
8.05	15	3.4%
26	15	3.4%
10.5	13	2.9%
7.775	11	2.5%
7.8542	9	2.0%
7.925	9	2.0%
7.2292	8	1.8%
Other values (173)	309	69.3%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	6	1.3%
4.0125	1	0.2%
6.2375	1	0.2%
6.4375	1	0.2%
6.45	1	0.2%
6.8583	1	0.2%
6.975	1	0.2%
7.05	2	0.4%
7.125	2	0.4%
7.225	4	0.9%

Value	Count	Frequency (%)
0	5	1.1%
6.4375	1	0.2%
6.45	1	0.2%
6.4958	1	0.2%
6.75	1	0.2%
6.95	1	0.2%
6.975	1	0.2%
7.05	4	0.9%
7.0542	1	0.2%
7.225	5	1.1%

Value	Count	Frequency (%)
0	5	1.1%
6.4375	1	0.2%
6.45	1	0.2%
6.4958	1	0.2%
6.75	1	0.2%
6.95	1	0.2%
6.975	1	0.2%
7.05	4	0.9%
7.0542	1	0.2%
7.225	5	1.1%

Value	Count	Frequency (%)
0	6	1.3%
4.0125	1	0.2%
6.2375	1	0.2%
6.4375	1	0.2%
6.45	1	0.2%
6.8583	1	0.2%
6.975	1	0.2%
7.05	2	0.4%
7.125	2	0.4%
7.225	4	0.9%

Cabin
['Text', 'Text']

	Dataset A	Dataset B
Distinct	87	88
Distinct (%)	87.0%	86.3%
Missing	346	344
Missing (%)	77.6%	77.1%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	15	15
Median length	3	3
Mean length	3.73	3.4901961
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	373	356
Distinct characters	19	19
Distinct categories	3	3 ?
Distinct scripts	2	2 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	75	77 ?
Unique (%)	75.0%	75.5%

Sample

	Dataset A	Dataset B
1st row	D35	B22
2nd row	A20	B94
3rd row	T	E17
4th row	C95	C110
5th row	B96 B98	E33

Value	Count	Frequency (%)
b96	3	2.5%
f	3	2.5%
b98	3	2.5%
b5	2	1.7%
b49	2	1.7%
e25	2	1.7%
c27	2	1.7%
c25	2	1.7%
c23	2	1.7%
g73	2	1.7%
Other values (88)	97	80.8%

Value	Count	Frequency (%)
g6	4	3.3%
b98	3	2.5%
b96	3	2.5%
f	3	2.5%
d	2	1.7%
c27	2	1.7%
c25	2	1.7%
c23	2	1.7%
b18	2	1.7%
e101	2	1.7%
Other values (89)	95	79.2%

Most occurring characters

Value	Count	Frequency (%)
B	38	10.2%
2	33	8.8%
3	29	7.8%
C	29	7.8%
1	27	7.2%
6	26	7.0%
4	22	5.9%
5	21	5.6%
8	21	5.6%
	20	5.4%
Other values (9)	107	28.7%

Value	Count	Frequency (%)
B	35	9.8%
2	31	8.7%
C	30	8.4%
3	30	8.4%
6	24	6.7%
1	24	6.7%
7	22	6.2%
8	20	5.6%
5	20	5.6%
	18	5.1%
Other values (9)	102	28.7%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	233	62.5%
Uppercase Letter	120	32.2%
Space Separator	20	5.4%

Value	Count	Frequency (%)
Decimal Number	218	61.2%
Uppercase Letter	120	33.7%
Space Separator	18	5.1%

Most frequent character per category

Uppercase Letter

Value	Count	Frequency (%)
B	38	31.7%
C	29	24.2%
D	17	14.2%
E	15	12.5%
F	9	7.5%
A	8	6.7%
G	3	2.5%
T	1	0.8%

Value	Count	Frequency (%)
B	35	29.2%
C	30	25.0%
D	16	13.3%
E	15	12.5%
F	8	6.7%
A	8	6.7%
G	7	5.8%
T	1	0.8%

Decimal Number

Value	Count	Frequency (%)
2	33	14.2%
3	29	12.4%
1	27	11.6%
6	26	11.2%
4	22	9.4%
5	21	9.0%
8	21	9.0%
9	19	8.2%
7	18	7.7%
0	17	7.3%

Value	Count	Frequency (%)
2	31	14.2%
3	30	13.8%
6	24	11.0%
1	24	11.0%
7	22	10.1%
8	20	9.2%
5	20	9.2%
9	17	7.8%
0	16	7.3%
4	14	6.4%

Space Separator

Value	Count	Frequency (%)
	20	100.0%

Value	Count	Frequency (%)
	18	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	253	67.8%
Latin	120	32.2%

Value	Count	Frequency (%)
Common	236	66.3%
Latin	120	33.7%

Most frequent character per script

Latin

Value	Count	Frequency (%)
B	38	31.7%
C	29	24.2%
D	17	14.2%
E	15	12.5%
F	9	7.5%
A	8	6.7%
G	3	2.5%
T	1	0.8%

Value	Count	Frequency (%)
B	35	29.2%
C	30	25.0%
D	16	13.3%
E	15	12.5%
F	8	6.7%
A	8	6.7%
G	7	5.8%
T	1	0.8%

Common

Value	Count	Frequency (%)
2	33	13.0%
3	29	11.5%
1	27	10.7%
6	26	10.3%
4	22	8.7%
5	21	8.3%
8	21	8.3%
	20	7.9%
9	19	7.5%
7	18	7.1%

Value	Count	Frequency (%)
2	31	13.1%
3	30	12.7%
6	24	10.2%
1	24	10.2%
7	22	9.3%
8	20	8.5%
5	20	8.5%
	18	7.6%
9	17	7.2%
0	16	6.8%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	373	100.0%

Value	Count	Frequency (%)
ASCII	356	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
B	38	10.2%
2	33	8.8%
3	29	7.8%
C	29	7.8%
1	27	7.2%
6	26	7.0%
4	22	5.9%
5	21	5.6%
8	21	5.6%
	20	5.4%
Other values (9)	107	28.7%

Value	Count	Frequency (%)
B	35	9.8%
2	31	8.7%
C	30	8.4%
3	30	8.4%
6	24	6.7%
1	24	6.7%
7	22	6.2%
8	20	5.6%
5	20	5.6%
	18	5.1%
Other values (9)	102	28.7%

Embarked
Categorical

	Dataset A	Dataset B
Distinct	3	3
Distinct (%)	0.7%	0.7%
Missing	1	0
Missing (%)	0.2%	0.0%
Memory size	7.0 KiB	7.0 KiB

S	318
C	88
Q	39

S	319
C	87
Q	40

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	445	446
Distinct characters	3	3
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	S	S
2nd row	S	S
3rd row	S	C
4th row	S	S
5th row	S	S

Common Values

Value	Count	Frequency (%)
S	318	71.3%
C	88	19.7%
Q	39	8.7%
(Missing)	1	0.2%

Value	Count	Frequency (%)
S	319	71.5%
C	87	19.5%
Q	40	9.0%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
s	318	71.5%
c	88	19.8%
q	39	8.8%

Value	Count	Frequency (%)
s	319	71.5%
c	87	19.5%
q	40	9.0%

Most occurring characters

Value	Count	Frequency (%)
S	318	71.5%
C	88	19.8%
Q	39	8.8%

Value	Count	Frequency (%)
S	319	71.5%
C	87	19.5%
Q	40	9.0%

Most occurring categories

Value	Count	Frequency (%)
Uppercase Letter	445	100.0%

Value	Count	Frequency (%)
Uppercase Letter	446	100.0%

Most frequent character per category

Uppercase Letter

Value	Count	Frequency (%)
S	318	71.5%
C	88	19.8%
Q	39	8.8%

Value	Count	Frequency (%)
S	319	71.5%
C	87	19.5%
Q	40	9.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	445	100.0%

Value	Count	Frequency (%)
Latin	446	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
S	318	71.5%
C	88	19.8%
Q	39	8.8%

Value	Count	Frequency (%)
S	319	71.5%
C	87	19.5%
Q	40	9.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	445	100.0%

Value	Count	Frequency (%)
ASCII	446	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
S	318	71.5%
C	88	19.8%
Q	39	8.8%

Value	Count	Frequency (%)
S	319	71.5%
C	87	19.5%
Q	40	9.0%

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset A

A simple visualization of nullity by column.

Dataset B

A simple visualization of nullity by column.

Dataset A

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Dataset B

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows
Last rows

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
545	546	0	1	Nicholson, Mr. Arthur Ernest	male	64.00	0	0	693	26.0000	NaN	S
51	52	0	3	Nosworthy, Mr. Richard Cater	male	21.00	0	0	A/4. 39886	7.8000	NaN	S
286	287	1	3	de Mulder, Mr. Theodore	male	30.00	0	0	345774	9.5000	NaN	S
182	183	0	3	Asplund, Master. Clarence Gustaf Hugo	male	9.00	4	2	347077	31.3875	NaN	S
831	832	1	2	Richards, Master. George Sibley	male	0.83	1	1	29106	18.7500	NaN	S
525	526	0	3	Farrell, Mr. James	male	40.50	0	0	367232	7.7500	NaN	Q
330	331	1	3	McCoy, Miss. Agnes	female	NaN	2	0	367226	23.2500	NaN	Q
870	871	0	3	Balkic, Mr. Cerin	male	26.00	0	0	349248	7.8958	NaN	S
248	249	1	1	Beckwith, Mr. Richard Leonard	male	37.00	1	1	11751	52.5542	D35	S
599	600	1	1	Duff Gordon, Sir. Cosmo Edmund ("Mr Morgan")	male	49.00	1	0	PC 17485	56.9292	A20	C

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
745	746	0	1	Crosby, Capt. Edward Gifford	male	70.0	1	1	WE/P 5735	71.0000	B22	S
562	563	0	2	Norman, Mr. Robert Douglas	male	28.0	0	0	218629	13.5000	NaN	S
557	558	0	1	Robbins, Mr. Victor	male	NaN	0	0	PC 17757	227.5250	NaN	C
442	443	0	3	Petterson, Mr. Johan Emil	male	25.0	1	0	347076	7.7750	NaN	S
192	193	1	3	Andersen-Jensen, Miss. Carla Christine Nielsine	female	19.0	1	0	350046	7.8542	NaN	S
263	264	0	1	Harrison, Mr. William	male	40.0	0	0	112059	0.0000	B94	S
845	846	0	3	Abbing, Mr. Anthony	male	42.0	0	0	C.A. 5547	7.5500	NaN	S
677	678	1	3	Turja, Miss. Anna Sofia	female	18.0	0	0	4138	9.8417	NaN	S
651	652	1	2	Doling, Miss. Elsie	female	18.0	0	1	231919	23.0000	NaN	S
285	286	0	3	Stankovic, Mr. Ivan	male	33.0	0	0	349239	8.6625	NaN	C

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
539	540	1	1	Frolicher, Miss. Hedwig Margaritha	female	22.0	0	2	13568	49.5000	B39	C
609	610	1	1	Shutes, Miss. Elizabeth W	female	40.0	0	0	PC 17582	153.4625	C125	S
80	81	0	3	Waelens, Mr. Achille	male	22.0	0	0	345767	9.0000	NaN	S
347	348	1	3	Davison, Mrs. Thomas Henry (Mary E Finck)	female	NaN	1	0	386525	16.1000	NaN	S
168	169	0	1	Baumann, Mr. John D	male	NaN	0	0	PC 17318	25.9250	NaN	S
553	554	1	3	Leeni, Mr. Fahim ("Philip Zenni")	male	22.0	0	0	2620	7.2250	NaN	C
762	763	1	3	Barah, Mr. Hanna Assi	male	20.0	0	0	2663	7.2292	NaN	C
700	701	1	1	Astor, Mrs. John Jacob (Madeleine Talmadge Force)	female	18.0	1	0	PC 17757	227.5250	C62 C64	C
860	861	0	3	Hansen, Mr. Claus Peter	male	41.0	2	0	350026	14.1083	NaN	S
267	268	1	3	Persson, Mr. Ernst Ulrik	male	25.0	1	0	347083	7.7750	NaN	S

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
659	660	0	1	Newell, Mr. Arthur Webster	male	58.0	0	2	35273	113.2750	D48	C
493	494	0	1	Artagaveytia, Mr. Ramon	male	71.0	0	0	PC 17609	49.5042	NaN	C
408	409	0	3	Birkeland, Mr. Hans Martin Monsen	male	21.0	0	0	312992	7.7750	NaN	S
55	56	1	1	Woolner, Mr. Hugh	male	NaN	0	0	19947	35.5000	C52	S
246	247	0	3	Lindahl, Miss. Agda Thorilda Viktoria	female	25.0	0	0	347071	7.7750	NaN	S
355	356	0	3	Vanden Steen, Mr. Leo Peter	male	28.0	0	0	345783	9.5000	NaN	S
266	267	0	3	Panula, Mr. Ernesti Arvid	male	16.0	4	1	3101295	39.6875	NaN	S
604	605	1	1	Homer, Mr. Harry ("Mr E Haven")	male	35.0	0	0	111426	26.5500	NaN	C
9	10	1	2	Nasser, Mrs. Nicholas (Adele Achem)	female	14.0	1	0	237736	30.0708	NaN	C
734	735	0	2	Troupiansky, Mr. Moses Aaron	male	23.0	0	0	233639	13.0000	NaN	S

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	# duplicates
Dataset does not contain duplicate rows.

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	# duplicates
Dataset does not contain duplicate rows.

Overview

Variables

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Most occurring characters

Most occurring categories

Most frequent character per category

Space Separator

Lowercase Letter

Uppercase Letter

Other Punctuation

Open Punctuation

Close Punctuation

Dash Punctuation

Most occurring scripts

Most frequent character per script

Common

Latin

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Most occurring scripts

Most frequent character per script

Latin

Most occurring blocks

Most frequent character per block

ASCII

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Space Separator

Other Punctuation

Uppercase Letter

Lowercase Letter

Most occurring scripts

Most frequent character per script

Common

Latin

Most occurring blocks

Most frequent character per block

ASCII

Most occurring characters